Cited by 852Statistical Methods and Bayesian InferenceMeta-analysis and systematic reviewsStatistical Methods in Clinical Trials

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Effect sizes for growth-modeling analysis for controlled clinical trials in the same metric as for classical analysis

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Feingold, Alan·2009·Psychological Methods

DOI 10.1037/a0014699PMC PMC2712654

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Abstract

The use of growth-modeling analysis (GMA)--including hierarchical linear models, latent growth models, and general estimating equations--to evaluate interventions in psychology, psychiatry, and prevention science has grown rapidly over the last decade. However, an effect size associated with the difference between the trajectories of the intervention and control groups that captures the treatment effect is rarely reported. This article first reviews 2 classes of formulas for effect sizes associated with classical repeated-measures designs that use the standard deviation of either change scores or raw scores for the denominator. It then broadens the scope to subsume GMA and demonstrates that the independent groups, within-subjects, pretest-posttest control-group, and GMA designs all estimate the same effect size when the standard deviation of raw scores is uniformly used. Finally, the article shows that the correct effect size for treatment efficacy in GMA--the difference between the estimated means of the 2 groups at end of study (determined from the coefficient for the slope difference and length of study) divided by the baseline standard deviation--is not reported in clinical trials.

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“Effect sizes were estimated for time by condition interactions according to the method described by Feingold (2009) for multilevel models.”
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Authors: Feingold, A.
Affiliations: Oregon Social Learning Center (US)
Corresponding author: Feingold, Alan
Venue: Psychological Methods
Publisher: American Psychological Association (APA)
Published: 2009
Volume: 14
Issue: 1
Pages: 43-53
ISSN: 1082-989X (ISSN-L)
1939-1463
DOI: 10.1037/a0014699
PMC: PMC2712654
OpenAlex: W2089811587
Cited by: 851
References: 58
Citations by year: 2026: 56 · 2025: 60 · 2024: 61 · 2023: 54 · 2022: 56 · 2021: 72 · 2020: 61 · 2019: 47 · 2018: 56 · 2017: 51 · 2016: 65 · 2015: 65 · 2014: 63 · 2013: 35 · 2012: 24
FWCI: 13.56
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Psychological Methods (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD046364)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords: effect size, growth models, latent growth curve models, multilevel analysis, repeated measures designs, change scores, treatment effects, intervention efficacy, pretest-posttest control group design
MeSH terms: Humans, Psychology, Social, Research Design, Meta-Analysis as Topic, Reproducibility of Results, Models, Statistical, Linear Models, Treatment Outcome, Controlled Clinical Trials as Topic

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Effect sizes for growth-modeling analysis for controlled clinical trials in the same metric as for classical analysis

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